Nighttime lights are a uniquely human phenomenon. Every night we capture data on this form of human emissions with the Earth Observation System VIIRS. While we expect that this data will be able to tell us a great deal about the people living and working beneath the lights, there are still many factors we need to evaluate in order to back up those assumptions. In the course of this lesson, we will be evaluating how the number of observations used to generate the average monthly radiance can contribute the the month-to-month variability in the dataset.
It’s unlikely that a headlamp would be seen from space, but we don’t know that for sure. We do know that the random varability that events like this represent will be more impactful the when fewer observations are present. Rather then try to seek an answer to these questions it might be better to respect that the world is a complex place. For any automated task (like the one we are working on) will contain conditions that you are probably unaware of and couldn’t do much about, even if you did know they existed. Those assumptions, known and unknown, carry through the analysis and have the potential to skew your results in unexpected ways. Know that there is a lot you don’t know helps you keep you mind keen for more questions rather then set on a specific answer.
At the end of last lesson, we created a correlation between yearly average radiance and poverty/age at the census tract level for three different counties in Texas. While the trends were not convincing, we assumed that the positive confirmation to our hypothesis is still out there and by conducting a quality control check on the input data, we can pull out the truth.
Hopefully, the answer is yes because this is a educational tutorial after all. Yet, your answer may feel different if you were exploring this in a professional context. Looking into one questions means you can’t investigate another. It important to be aware of the Sunk Cost Fallacy and loss aversion as it affects your research. The more we invest into something, the less likely we are to back out.
The average monthly radiance values are delivered with an associated layer that reports the number of daily observations that were used to generate the mean monthly value for each observation location. These values can range from 0-31 depending on the month. We want to make sure that we are only looking at observation locations where we have a high degree of confidence that the values represent a true mean.
There are three reasons why this is worth evaluating.
View Angle
The VIIRS sensor captures data in a 3,000 km swath. This means there is a lot of area that is off nadir. As night lights are generally directional features (think lights on the side of the building), the angle at which the image is captured will affect the amount of radiance observed. The result of this effect is that some nightly images will have lower or higher values than the actual observed value at nadir. We can get around this problem by working only with on nadir passes, but those images occur only every 14 days or so. That timeframe severely limits our total number of observations and would require a whole new workflow based on the daily images. The second option would be to ensure you have enough observation to average out that variability. How much is enough? We’ll try to find out.
Lunar Radiance
The moon is a large roundish rock that has influenced culture and inspired the curiosity of people for as long as we’ve been around. We’ve always cared about the moon because it reflects the radiance of the sun back at the Earth. This reflectance means that we can see it. Due to the orbit patterns of the three celestial bodies, we end up with about half of each month being darker then the other half. The effects of lunar radiance are substantial in darker areas, especially when combined with freshly fallen snow. Our counts data does not tell us anything about the date of the image captured, but we’ll just cross our fingers and assume it’s normally distributed. Therefore, the more observations we get, the less we need to worry about the moon.